Enhancing chemistry-domain scientific paper summarization by knowledge graphs

Yutong Qu; Jian Yang; Weitong Chen; Yan Jiao; Lishan Yang; Congbo Ma

doi:10.1007/978-981-96-0814-0_21

Enhancing chemistry-domain scientific paper summarization by knowledge graphs

Yutong Qu^*
, Jian Yang
, Weitong Chen
, Yan Jiao
, Lishan Yang
, Congbo Ma

^*Corresponding author for this work

School of Computing

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding contribution › peer-review

Abstract

Scientific paper summarization has gained rapid growth in attention along with explosive demand for scientific research, especially in fields akin to chemistry, which is more challenging than typical summarization due to specialized terminologies, domain-specific information, and requirements on vital needs of scientific researchers. Inspired by the prominent capabilities shown by graph-structured data in conveying self-defined and a variety of information, we propose to leverage knowledge graphs to enhance scientific summarization in a chemistry scope. In this paper, we first construct a chemistry paper summarization dataset, Hydrogen Evolution Reaction with Carbon (HERC), which includes 2,254 selected chemical research papers under chemistry expert guidance. Each paper is associated with its citation information and 9 categories of expert-labelled knowledge. Based on that, we create citation and knowledge graphs for each paper. Furthermore, we develop a knowledge-aware summarizer that incorporates the information from both the citation and knowledge graphs and showcases the improved performance with the usage of knowledge graphs.

Original language	English
Title of host publication	Advanced Data Mining and Applications
Subtitle of host publication	20th International Conference, ADMA 2024, Sydney, NSW, Australia, December 3–5, 2024, proceedings, part II
Editors	Quan Z. Sheng, Gill Dobbie, Jing Jiang, Xuyun Zhang, Wei Emma Zhang, Yannis Manolopoulos, Jia Wu, Wathiq Mansoor, Congbo Ma
Place of Publication	Singapore
Publisher	Springer, Springer Nature
Pages	319-334
Number of pages	16
ISBN (Electronic)	9789819608140
ISBN (Print)	9789819608133
DOIs	https://doi.org/10.1007/978-981-96-0814-0_21
Publication status	Published - 2025
Event	20th International Conference on Advanced Data Mining Applications, ADMA 2024 - Sydney, Australia Duration: 3 Dec 2024 → 5 Dec 2024

Publication series

Name	Lecture Notes in Computer Science
Publisher	Springer
Volume	15388
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	20th International Conference on Advanced Data Mining Applications, ADMA 2024
Country/Territory	Australia
City	Sydney
Period	3/12/24 → 5/12/24

Keywords

Scientific paper summarization
Summarization dataset
Knowledge graph

Access to Document

10.1007/978-981-96-0814-0_21

Cite this

Qu, Y., Yang, J., Chen, W., Jiao, Y., Yang, L., & Ma, C. (2025). Enhancing chemistry-domain scientific paper summarization by knowledge graphs. In Q. Z. Sheng, G. Dobbie, J. Jiang, X. Zhang, W. E. Zhang, Y. Manolopoulos, J. Wu, W. Mansoor, & C. Ma (Eds.), Advanced Data Mining and Applications: 20th International Conference, ADMA 2024, Sydney, NSW, Australia, December 3–5, 2024, proceedings, part II (pp. 319-334). (Lecture Notes in Computer Science; Vol. 15388). Springer, Springer Nature. https://doi.org/10.1007/978-981-96-0814-0_21

Qu, Yutong ; Yang, Jian ; Chen, Weitong et al. / Enhancing chemistry-domain scientific paper summarization by knowledge graphs. Advanced Data Mining and Applications: 20th International Conference, ADMA 2024, Sydney, NSW, Australia, December 3–5, 2024, proceedings, part II. editor / Quan Z. Sheng ; Gill Dobbie ; Jing Jiang ; Xuyun Zhang ; Wei Emma Zhang ; Yannis Manolopoulos ; Jia Wu ; Wathiq Mansoor ; Congbo Ma. Singapore : Springer, Springer Nature, 2025. pp. 319-334 (Lecture Notes in Computer Science).

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abstract = "Scientific paper summarization has gained rapid growth in attention along with explosive demand for scientific research, especially in fields akin to chemistry, which is more challenging than typical summarization due to specialized terminologies, domain-specific information, and requirements on vital needs of scientific researchers. Inspired by the prominent capabilities shown by graph-structured data in conveying self-defined and a variety of information, we propose to leverage knowledge graphs to enhance scientific summarization in a chemistry scope. In this paper, we first construct a chemistry paper summarization dataset, Hydrogen Evolution Reaction with Carbon (HERC), which includes 2,254 selected chemical research papers under chemistry expert guidance. Each paper is associated with its citation information and 9 categories of expert-labelled knowledge. Based on that, we create citation and knowledge graphs for each paper. Furthermore, we develop a knowledge-aware summarizer that incorporates the information from both the citation and knowledge graphs and showcases the improved performance with the usage of knowledge graphs.",

keywords = "Scientific paper summarization, Summarization dataset, Knowledge graph",

author = "Yutong Qu and Jian Yang and Weitong Chen and Yan Jiao and Lishan Yang and Congbo Ma",

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doi = "10.1007/978-981-96-0814-0\_21",

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Qu, Y, Yang, J, Chen, W, Jiao, Y, Yang, L & Ma, C 2025, Enhancing chemistry-domain scientific paper summarization by knowledge graphs. in QZ Sheng, G Dobbie, J Jiang, X Zhang, WE Zhang, Y Manolopoulos, J Wu, W Mansoor & C Ma (eds), Advanced Data Mining and Applications: 20th International Conference, ADMA 2024, Sydney, NSW, Australia, December 3–5, 2024, proceedings, part II. Lecture Notes in Computer Science, vol. 15388, Springer, Springer Nature, Singapore, pp. 319-334, 20th International Conference on Advanced Data Mining Applications, ADMA 2024, Sydney, Australia, 3/12/24. https://doi.org/10.1007/978-981-96-0814-0_21

Enhancing chemistry-domain scientific paper summarization by knowledge graphs. / Qu, Yutong; Yang, Jian; Chen, Weitong et al.
Advanced Data Mining and Applications: 20th International Conference, ADMA 2024, Sydney, NSW, Australia, December 3–5, 2024, proceedings, part II. ed. / Quan Z. Sheng; Gill Dobbie; Jing Jiang; Xuyun Zhang; Wei Emma Zhang; Yannis Manolopoulos; Jia Wu; Wathiq Mansoor; Congbo Ma. Singapore: Springer, Springer Nature, 2025. p. 319-334 (Lecture Notes in Computer Science; Vol. 15388).

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding contribution › peer-review

TY - GEN

T1 - Enhancing chemistry-domain scientific paper summarization by knowledge graphs

AU - Qu, Yutong

AU - Yang, Jian

AU - Chen, Weitong

AU - Jiao, Yan

AU - Yang, Lishan

AU - Ma, Congbo

PY - 2025

Y1 - 2025

N2 - Scientific paper summarization has gained rapid growth in attention along with explosive demand for scientific research, especially in fields akin to chemistry, which is more challenging than typical summarization due to specialized terminologies, domain-specific information, and requirements on vital needs of scientific researchers. Inspired by the prominent capabilities shown by graph-structured data in conveying self-defined and a variety of information, we propose to leverage knowledge graphs to enhance scientific summarization in a chemistry scope. In this paper, we first construct a chemistry paper summarization dataset, Hydrogen Evolution Reaction with Carbon (HERC), which includes 2,254 selected chemical research papers under chemistry expert guidance. Each paper is associated with its citation information and 9 categories of expert-labelled knowledge. Based on that, we create citation and knowledge graphs for each paper. Furthermore, we develop a knowledge-aware summarizer that incorporates the information from both the citation and knowledge graphs and showcases the improved performance with the usage of knowledge graphs.

AB - Scientific paper summarization has gained rapid growth in attention along with explosive demand for scientific research, especially in fields akin to chemistry, which is more challenging than typical summarization due to specialized terminologies, domain-specific information, and requirements on vital needs of scientific researchers. Inspired by the prominent capabilities shown by graph-structured data in conveying self-defined and a variety of information, we propose to leverage knowledge graphs to enhance scientific summarization in a chemistry scope. In this paper, we first construct a chemistry paper summarization dataset, Hydrogen Evolution Reaction with Carbon (HERC), which includes 2,254 selected chemical research papers under chemistry expert guidance. Each paper is associated with its citation information and 9 categories of expert-labelled knowledge. Based on that, we create citation and knowledge graphs for each paper. Furthermore, we develop a knowledge-aware summarizer that incorporates the information from both the citation and knowledge graphs and showcases the improved performance with the usage of knowledge graphs.

KW - Scientific paper summarization

KW - Summarization dataset

KW - Knowledge graph

UR - https://www.scopus.com/pages/publications/85213350108

U2 - 10.1007/978-981-96-0814-0_21

DO - 10.1007/978-981-96-0814-0_21

M3 - Conference proceeding contribution

AN - SCOPUS:85213350108

SN - 9789819608133

T3 - Lecture Notes in Computer Science

SP - 319

EP - 334

BT - Advanced Data Mining and Applications

A2 - Sheng, Quan Z.

A2 - Dobbie, Gill

A2 - Jiang, Jing

A2 - Zhang, Xuyun

A2 - Zhang, Wei Emma

A2 - Manolopoulos, Yannis

A2 - Wu, Jia

A2 - Mansoor, Wathiq

A2 - Ma, Congbo

PB - Springer, Springer Nature

CY - Singapore

T2 - 20th International Conference on Advanced Data Mining Applications, ADMA 2024

Y2 - 3 December 2024 through 5 December 2024

ER -

Qu Y, Yang J, Chen W, Jiao Y, Yang L, Ma C. Enhancing chemistry-domain scientific paper summarization by knowledge graphs. In Sheng QZ, Dobbie G, Jiang J, Zhang X, Zhang WE, Manolopoulos Y, Wu J, Mansoor W, Ma C, editors, Advanced Data Mining and Applications: 20th International Conference, ADMA 2024, Sydney, NSW, Australia, December 3–5, 2024, proceedings, part II. Singapore: Springer, Springer Nature. 2025. p. 319-334. (Lecture Notes in Computer Science). doi: 10.1007/978-981-96-0814-0_21